Validation of imaging with pathology in laryngeal cancer: accuracy of the registration methodology.

PURPOSE To investigate the feasibility and accuracy of an automated method to validate gross tumor volume (GTV) delineations with pathology in laryngeal and hypopharyngeal cancer. METHODS AND MATERIALS High-resolution computed tomography (CT(HR)), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans were obtained from 10 patients before total laryngectomy. The GTV was delineated separately in each imaging modality. The laryngectomy specimen was sliced transversely in 3-mm-thick slices, and whole-mount hematoxylin-eosin stained (H&E) sections were obtained. A pathologist delineated tumor tissue in the H&E sections (GTV(PATH)). An automatic three-dimensional (3D) reconstruction of the specimen was performed, and the CT(HR), MRI, and PET were semiautomatically and rigidly registered to the 3D specimen. The accuracy of the pathology-imaging registration and the specimen deformation and shrinkage were assessed. The tumor delineation inaccuracies were compared with the registration errors. RESULTS Good agreement was observed between anatomical landmarks in the 3D specimen and in the in vivo images. Limited deformations and shrinkage (3% ± 1%) were found inside the cartilage skeleton. The root mean squared error of the registration between the 3D specimen and the CT, MRI, and PET was on average 1.5, 3.0, and 3.3 mm, respectively, in the cartilage skeleton. The GTV(PATH) volume was 7.2 mL, on average. The GTVs based on CT, MRI, and PET generated a mean volume of 14.9, 18.3, and 9.8 mL and covered the GTV(PATH) by 85%, 88%, and 77%, respectively. The tumor delineation inaccuracies exceeded the registration error in all the imaging modalities. CONCLUSIONS Validation of GTV delineations with pathology is feasible with an average overall accuracy below 3.5 mm inside the laryngeal skeleton. The tumor delineation inaccuracies were larger than the registration error. Therefore, an accurate histological validation of anatomical and functional imaging techniques for GTV delineation is possible in laryngeal cancer patients.

[1]  Uulke A. van der Heide,et al.  Simultaneous multi-modality ROI delineation in clinical practice , 2009, Comput. Methods Programs Biomed..

[2]  Gerda M Verduijn,et al.  Magnetic resonance imaging protocol optimization for delineation of gross tumor volume in hypopharyngeal and laryngeal tumors. , 2009, International journal of radiation oncology, biology, physics.

[3]  Sotirios Bisdas,et al.  18F-Fluorodeoxyglucose-PET/CT to evaluate tumor, nodal disease, and gross tumor volume of oropharyngeal and oral cavity cancer: comparison with MR imaging and validation with surgical specimen , 2009, Neuroradiology.

[4]  Jean-François Daisne,et al.  Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. , 2004, Radiology.

[5]  Max A. Viergever,et al.  Adaptive Stochastic Gradient Descent Optimisation for Image Registration , 2009, International Journal of Computer Vision.

[6]  Peter H Ahn,et al.  Positron emission tomography/computed tomography for target delineation in head and neck cancers. , 2008, Seminars in nuclear medicine.

[7]  M van Herk,et al.  The potential impact of CT-MRI matching on tumor volume delineation in advanced head and neck cancer. , 1997, International journal of radiation oncology, biology, physics.

[8]  J J W Lagendijk,et al.  A novel method for comparing 3D target volume delineations in radiotherapy , 2008, Physics in medicine and biology.

[9]  M J Yaffe,et al.  Whole‐specimen histopathology: a method to produce whole‐mount breast serial sections for 3‐D digital histopathology imaging , 2007, Histopathology.

[10]  L. Boersma,et al.  Analysis of the relative deformation of lung lobes before and after surgery in patients with NSCLC , 2009, Physics in medicine and biology.

[11]  Marco van Vulpen,et al.  Validation of functional imaging with pathology for tumor delineation in the prostate. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  Sandra Nuyts,et al.  Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: correlation between radiologic and histopathologic findings. , 2007, International journal of radiation oncology, biology, physics.

[13]  M. Yaffe,et al.  Developing a methodology for three-dimensional correlation of PET–CT images and whole-mount histopathology in non-small-cell lung cancer , 2008, Current oncology.

[14]  Marcel van Herk,et al.  Decreased 3D observer variation with matched CT-MRI, for target delineation in Nasopharynx cancer , 2010, Radiation oncology.

[15]  C. Raaijmakers,et al.  Interobserver variability of clinical target volume delineation of glandular breast tissue and of boost volume in tangential breast irradiation. , 2005, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  Marcel van Herk,et al.  Target definition in prostate, head, and neck. , 2005, Seminars in radiation oncology.

[17]  Alicia Y Toledano,et al.  An evaluation of the variability of tumor-shape definition derived by experienced observers from CT images of supraglottic carcinomas (ACRIN protocol 6658). , 2007, International journal of radiation oncology, biology, physics.

[18]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[19]  P. Dulguerov,et al.  Neoplastic invasion of laryngeal cartilage: reassessment of criteria for diagnosis at MR imaging. , 2008, Radiology.

[20]  Liesbeth Boersma,et al.  Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. , 2007, International journal of radiation oncology, biology, physics.